TickAtlas

Python Forex API — One requests.get() Away

Pull live indicators, OHLCV, and market summaries into Python with a single GET. Responses drop straight into pandas DataFrames.

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No credit card$2.50 PAYG credit on sign up
15+API endpoints
42Indicator series
M1–D17 timeframes
$2.50PAYG starting credit
Why TickAtlas

Why Use TickAtlas?

Pre-Calculated, Redis-Cached

Indicator values are computed server-side on every new candle. Your app queries the result — no TA library, no candle history needed.

7 Timeframes

M1, M5, M15, M30, H1, H4, D1 — the same endpoint serves scalpers and position traders alike.

Forex, Crypto, Commodities

EURUSD, GBPUSD, XAUUSD, BTCUSD, USDJPY — data available for every symbol in our data.

One Request. Instant Data.

REQUEST 
import requests

response = requests.get(
    "https://tickatlas.com/v1/indicator",
    headers={"X-API-Key": "YOUR_API_KEY"},
    params={
        "symbol": "EURUSD",
        "indicator": "RSI_14",
        "timeframe": "H1"
    }
)
data = response.json()
print(data["data"]["value"])
200 OK 
{
  "success": true,
  "data": {
    "symbol": "EURUSD",
    "timeframe": "H1",
    "indicator": "RSI_14",
    "value": 62.48,
    "bid": 1.08401,
    "ask": 1.08414,
    "updated_at": 1711548000,
    "server_time": "2024-03-27T14:00:00+00:00"
  }
}

Three Ways to Use It

Basic Single indicator fetch
pandas Multi-symbol DataFrame
Trading Bot Multi-indicator confluence scanner

What You Can Build

Automated Trading Bot

Scan symbols, detect confluence, post to Discord or fire broker orders via your Python trading library — all in Python with TickAtlas providing the indicator data layer.

Jupyter Research

Fetch live indicator snapshots into a DataFrame in a Jupyter notebook. Compare multiple symbols, build correlation matrices, test hypothesis ideas without downloading historical data.

FastAPI / Flask Backend

Build a market data microservice that proxies TickAtlas to your own frontend, transforms data, or caches it for internal dashboards. Python backend, minimal code.

Compatible Python libraries

requests httpx aiohttp pandas polars numpy Jupyter FastAPI Flask Celery

Plug into any stack

  • ChatGPT Custom GPTs
  • Claude Tools
  • Python / pandas
  • Node.js
  • Discord bots
  • Slack webhooks
  • n8n
  • Zapier
  • Google Sheets
Pricing

Start on pay-as-you-go, upgrade when the traffic justifies it.

Every new account starts with $2.50 of pay-as-you-go credit and no card. Monthly plans add a flat monthly price, WebSocket streaming and support.

Pay as you go
$2.50 credit No card required to start
  • $2.50 of credit included
  • Every REST endpoint except raw ticks
  • No card required, no overage
  • 10 API keys
See plan details
Starter
$29 per month · 10,000 requests/day · 120/min
  • Every REST endpoint except raw ticks
  • WebSocket streaming, 5 symbols
  • Released calendar actuals
  • Email support
  • 3 API keys
See plan details
Popular
Pro
$79 per month · 100,000 requests/day · 600/min
  • Everything in Starter
  • Raw tick data (/v1/ticks)
  • Priority support
  • 10 API keys
  • WebSocket streaming, 20 symbols
See plan details
Docs and implementation

Every next step is one click away.

Landing pages convert; documentation closes the loop. These are the routes a developer usually needs next.

Documentation hub

Start from the developer portal and map the API families your product needs.

Open docs

Quickstart

Make the first authenticated request and validate the setup end to end.

Read quickstart

API reference

Request and response contracts for quote, indicator, summary and calendar.

Browse reference

Integrations

How the API drops into ChatGPT, Claude, Discord, n8n, Zapier and Sheets.

See integrations
Frequently asked questions

What developers ask first.

Straight answers on limits, coverage and how the endpoint behaves in production.

What Python dependencies do I need?

Only the standard requests library for basic usage. For pandas integration, add pandas. For async usage, use aiohttp or httpx. No TA-Lib, no CCXT, no trading-specific libraries required.

How do I load indicator history into a pandas DataFrame?

Call GET /v1/indicator/history with your chosen symbol, indicator, and timeframe. The response returns a list of {time, value} objects. Pass it to pd.DataFrame() and set_index('time') for a time-indexed series ready for analysis.

Can I use this with async Python (asyncio / aiohttp)?

Yes. Replace requests.get() with aiohttp.ClientSession().get() or httpx.AsyncClient().get(). The API is standard REST with JSON — fully compatible with any async HTTP client.

Is there a Python SDK?

Not yet — but the API is simple enough that a 5-line helper function is all you need, as shown in the examples above. A typed Python client may be added in future. Follow the changelog at tickatlas.com for updates.

Start building

Start Building in 5 Minutes

Sign up, grab your API key, make your first request.

Every new account starts with $2.50 of pay-as-you-go credit. No card required.

Get $2.50 free credit Read the Docs